Journal
MOLECULAR & CELLULAR PROTEOMICS
Volume 18, Issue 1, Pages 162-168Publisher
AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
DOI: 10.1074/mcp.TIR118.000947
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Funding
- NIGMS of the NIH [DP2GM123497]
- SPARC grant from the Broad Institute
- NEU
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [DP2GM123497] Funding Source: NIH RePORTER
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Many proteoforms-arising from alternative splicing, post-translational modifications (PTM), or paralogous genes- have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (Hlquant) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by Hlquant and derived an algorithm for optimal inference. We applied this algorithm to infer proteoform stoichiometries in two experimental systems that supported rigorous bench-marking: alkylated proteoforms spiked-in at known ratios and endogenous histone 3 PTM proteoforms quantified relative to internal heavy standards. When compared with the benchmarks, the proteoform stoichiometries interfered by Hlquant without using external standards had relative error of 5-15% for simple proteoforms and 20-30% for complex proteoforms.
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